Social Media Influence: Metrics Matter

Social Media Influence: Metrics Matter

BY Juliana Houldcroft

ADVISOR • Sharmin Attaran

EDITORIAL REVIEWER • Elaine Notarantonio

_________________________________________________________________________________________

Submitted in partial fulfillment of the requirements for graduation with honors in the Bryant University Honors Program

APRIL 2017

TABLE OF CONTENTS Abstract ............................................................................................................................... 1 Introduction ......................................................................................................................... 2 Background ......................................................................................................................... 3 Literature Review ................................................................................................................ 3 Social Media Marketing .............................................................................................. 3 Electronic Word-of-Mouth ......................................................................................... 4 Elaboration Likelihood Model .................................................................................... 6 Online Influence .......................................................................................................... 7 Perceived Credibility Online ....................................................................................... 8 Influence & Social Media Metrics ............................................................................ 10 Conclusion ................................................................................................................ 13 Hypotheses ........................................................................................................................ 14 Methods ............................................................................................................................. 14 Analysis & Results ............................................................................................................ 17 Discussion & Implications ................................................................................................ 22 Limitations & Future Research ......................................................................................... 27 Conclusion ........................................................................................................................ 28 Appendices ........................................................................................................................ 29 Appendix A- High and Low Metrics Condition ....................................................... 29 Appendix B- Manipulation Check Survey Items ...................................................... 31 Appendix C- Interaction Intention Survey Items ...................................................... 32 Appendix D- Perceived Credibility Survey Items .................................................... 33 Appendix E- Perceived Information Quality & Usefulness Survey Items ............... 34 Appendix F- Demographic & Exploratory Survey Items ......................................... 35 Appendix G- Key Demographics Breakdown .......................................................... 37 Appendix H- Manipulation Check Analysis ............................................................. 38 Appendix I- Exploratory Findings ............................................................................ 40 References ......................................................................................................................... 41

Social Media Influence: Metrics Matter Senior Capstone Project for Juliana Houldcroft

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ABSTRACT It is imperative for companies to engage in social media marketing as consumers are often

dependent on online information and electronic word-of-mouth. Past literature claims that

consumers evaluate the influence of communications differently on social media than they

would in a traditional environment because of the nature of the internet. This study aims to

analyze user’s perceptions of social media marketing influence and determines if user’s

perception of influence changes based on the number of social media metrics (likes,

comments, and shares) that accompany a Facebook post. The study also investigates if

perceptions of influence vary depending on a user’s level of involvement in the situation. A

2x2 factorial design is utilized to manipulate both level of involvement and amount of likes,

comments, and shares that accompany a Facebook post. The results contend that a high

number of likes, comments, and shares on Facebook leads to increased perceptions of source

credibility and information usefulness. In particular, the results prove that a high number of

likes, comments, and shares on Facebook leads to increased purchase intention in a low-

involvement situation. These results are essential to marketers as they prove the importance of

curating engaging content on company’s Facebook pages in order to generate high amounts of

likes, comments, and shares. Increasing the amount of likes, comments, and shares on

Facebook will make the post more influential to users.


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INTRODUCTION As the internet and social media are becoming more popular it is essential for businesses to

adapt to the changing times in terms of their marketing initiatives. Consumers are seldom only

influenced by traditional advertisements today as most of their attention is focused online. In

order to stay competitive, companies must adjust their marketing strategy and leverage social

media and electronic word of mouth to their advantage. Oftentimes the online environment

today is referred to as the “Like Economy” (Gerlitz & Helmond, 2011). The “Like” button

serves as an online metric that adds content validation to websites (Gerlitz & Helmond, 2011).

There are also several other visible social media metrics in the online sphere that serve as a

way for companies to essentially assess their worth (Baym, 2013). Some research has been

done on these social media metrics but it remains unclear exactly how important these metrics

are when it comes to evaluating the influence of online communications.

Businesses today are tasked with understanding the importance of their social media presence

in the “Like Economy.” In order to do so, businesses must understand which particular social

media qualities affect consumer’s perceptions of their message. In the online environment, it

is often difficult for users to make judgements based on the minimal amount of information

provided in a social media context (Morris, Counts, Roseway, Hoff & Schwarz, 2012).

Therefore, users often rely on other heuristic clues to evaluate the effectiveness of social

media communications (Morris et al., 2012). Companies must be aware of these heuristic

clues so that they can ensure they are present in their marketing messages.

When studying the influence of a particular social media message it is important to note that a

consumer’s level of involvement often impacts which information processing route they use.

The elaboration likelihood model developed by Petty and Cacioppo in 1981 contends that a

consumer processes information either centrally or peripherally depending on their level of

involvement. It can therefore be assumed that users may judge information online differently

depending on their level of involvement. It is imperative for businesses to understand how

these different situations can impact customer’s perceptions of their message. Since the

concept of social media metrics and influence has not yet been widely researched, it is pivotal


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to bridge the gap between traditional concepts of influence and the social media metrics that

are the manifestation of influence in the online environment.

BACKGROUND Social media is defined as forms of electronic communication through which users create

online communities to share information, ideas, personal messages, and other content

(Merriam-Webster Dictionary). As of February 2017, Facebook is the number one most

popular social networking site. This ranking is based on the network’s global traffic rank and

U.S. traffic rank (Top 15 Most Popular Social Networking Sites). Facebook was founded in

2004 and has 1.23 billion daily active users on average as of December 2016. Facebook also

had an average of 1.15 billion mobile daily active users as of December 2016. As far as

monthly users, the statistics are 1.86 billion and 1.74 billion respectively (Facebook

Newsroom). Three common Facebook metrics include the “like”, “comment”, and “share”.

The “like” servers as a way to let another user know you enjoyed something without leaving a

comment. Users who view the post afterwards will be able to see you liked it and a story may

be posted on your timeline indicating that you liked the post (Facebook Help Center). A

“comment” is similar to a “like” and allows the user to say something publicly about the post

(Giffard-Tiney, 2015). A “share” is essentially a way to show content one saw somewhere to

the rest of one’s friends (Giffard-Tiney, 2015). Likes, comments, and shares are three of the

most popular metrics on Facebook and each indicates a degree of engagement. These social

buttons also display a numeric counter that shows other users how many times the content has

been liked, commented, or shared.

LITERATURE REVIEW Social Media Marketing

As people are spending more time online shopping and browsing, businesses are almost

forced to maintain a social media presence in order to remain competitive in the changing

times. A study performed by Brettel, Reich, Gavilanes & Flatten (2015) found that advertising

on Facebook can significantly affect sales. This is because the engaging nature of social

media enables companies to retain customers and encourage them to repurchase. More


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specifically, Brettel et al. (2015) found that Facebook advertising positively affects sales by

generating “likes” and “comments” from users. Ultimately, social media marketing has a

positive impact on the bottom line. Social media has become essential to the marketing

concept as it allows companies to interact directly with their customers (Wong, 2017).

Presumably, the nature of social media communications elicits a more personal connection

between the consumer and the brand. The formation of this relationship leads to subsequent

brand loyalty (DeMers, 2017).

Social media marketing provides a wealth of benefits to businesses at virtually no cost. It

fosters richer customer experiences as companies can publicly demonstrate their dedication to

customer service (DeMers, 2017). It increases brand recognition by serving as an outlet for

increased visibility and enables a company to obtain richer customer insights through social

listening (DeMers, 2017). Social media is also unique in that the more engagement that is

generated via likes and comments, the more attention the message is given (Brettel et al.,

2015). This is part of what makes advertising particularly successful on Facebook.

Essentially, a company has everything to gain and not much to lose by engaging in social

media marketing (DeMers, 2017).

Mangold and Faulds (2009) proposed that social media be considered a hybrid component of

the promotional mix because it enables communication in both a traditional and non-

traditional sense. In a traditional sense, it enables companies to communicate directly with

their customers. Perhaps more importantly, social media also enables customers of a

particular business to talk to other customers as well (Mangold & Faulds, 2009). In this way,

social media also facilitates electronic word-of-mouth in various ways including commenting

and sharing.

Electronic Word-of-Mouth

Electronic word-of-mouth is often thought of as a new form of social communication content

involving information-seeking customers and information-sharing customers (Cheung &

Thadani, 2012). Electronic word-of-mouth in particular has become fundamental to the


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marketing concept. As technology and society are rapidly advancing, traditional

advertisements are viewed far less-favorably. Social media facilitates electronic word-of-

mouth by providing an outlet for consumers to interact with each other. Users are able to

spread electronic word-of-mouth by commenting or liking which both serve as indicators of

content quality (Brettel et al., 2015). Past literature investigating electronic word-of-mouth

has maintained that it is far more influential than its print ad counterparts (Cheung & Thadani,

2012). Sun, Youn, Wu and Kuntaraporn (2006) point to the ability of electronic word of

mouth to influence the consumer decision-making process. More specifically, the study builds

on the notion that electronic word of mouth is often seen as more influential due to speed,

convenience, one-to-many reach, and absence of face-to-face pressure (Phelps, Lewis,

Mobilio, Perry & Raman, 2004). Gupta & Harris (2010) investigated the role electronic word

of mouth plays in both high and low involvement situations. They used survey research that

measured purchase intention based on quality and quantity of word of mouth

recommendations in both high and low involvement situations. The methodology employed

was a simulated shopping environment and the research concluded that in a high-involvement

situation, electronic word of mouth serves as an additional argument that is carefully

considered with the rest of the information whereas in a low-involvement situation, electronic

word of mouth serves as more of a decision heuristic. Despite a consumer’s level of

motivation in a given situation, electronic word of mouth is nonetheless integral to the

consumer decision-making process. More often than not, people make offline decisions on the

basis of available online information (Godes & Mayzlin, 2004). This is not to say that

consumers automatically accept all electronic word of mouth as fact. Perceived credibility of

a source and quality of information significantly impact electronic word of mouth adoption as

well (Fan, Miao, Fang & Lin, 2013). More specifically, the perceived quality of electronic

word-of-mouth communications impacts perceived credibility which in turn impacts adoption

(Fan et al., 2013). This is why it is imperative for businesses to both monitor and engage in

electronic word of mouth related to their brand in order to achieve optimal impact. It is also

essential for businesses to understand what social media factors contribute to perceived

credibility and quality of information.


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Theoretical Background- Elaboration Likelihood Model

The elaboration likelihood model (ELM) was first developed by Petty and Cacioppo in 1981

as a way to account for reported differences in influence across different individuals and

contexts. Essentially the elaboration likelihood model attempts to explain the way people

process persuasive communications. The ELM contends that a message can influence a

person’s attitude either centrally or peripherally. The central route refers to the nature of the

argument in the message while the peripheral route focuses on issues or themes that are not

directly related to the subject matter of the message (Petty & Cacioppo, 1981).

In 1984, Petty and Cacioppo investigated how the Elaboration Likelihood Model accounts for

the effects of source factors on persuasion. In this study, motivation was measured by

manipulating both relevance and consequence of the scenario. The findings indicated that

when a person is unmotivated or unable to process a message fully they employ the peripheral

route to persuasion and rely on simple clues such as source expertise and attractiveness. When

they are highly motivated they employ the central route to persuasion and strong arguments

are more effective than weak arguments and peripheral clues do not matter. When they are

moderately motivated, subjects utilize various clues in the persuasion process including

source factors (Petty & Cacioppo, 1984).

In 1986, Petty and Cacioppo built off of their earlier work and investigated the application of

the elaboration likelihood model in both high-involvement and low-involvement situations.

Their study placed some users in a high-involvement situation in which the users were led to

believe that the advertised product they were about to see would soon be available in their

local area and that they would be able to take it home after the survey as a gift. The low-

involvement situation conveyed to users that the product they were about to see would not be

available in their local area in the near future and that they would be allowed to take home a

different product than was being advertised after the survey. The study proved that under the

high-involvement situation, users were influenced by the advertisement content and the

presence or absence of a celebrity endorser carried little weight. Under the low-involvement

situation, the presence of a celebrity endorser was imperative. This research contends that


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source credibility is of greater importance in a low-involvement situation than a high-

involvement situation. This study also confirms that involvement and motivation are essential

to a consumer selecting a route to persuasion (Petty & Cacioppo, 1986).

Sussman and Siegal (2003) explored the elaboration likelihood model in computer-mediated

contexts. They explored the extent to which opinion seekers are willing to adopt the

recommendations of others in an organization. The study focused on argument quality and

source credibility. Although source credibility is typically viewed simply as a decision

heuristic clue, the findings indicated that it is more complex than that. The study found that

source credibility also serves as an additional influential factor for individuals following the

central route to persuasion as well (Sussman & Siegal, 2013). SanJose-Cabezudo, Gutierrez-

Arranz, and Gutierrez-Cillan (2009) found similar results when they investigated the

combined influence of the central and peripheral route in a high-involvement context.

Although past research claimed peripheral clues are only influential in low-involvement

situations, this study contended that peripheral clues online can also impact an individual’s

attitudes in a high-involvement situation.

Online Influence

Influence is the capacity to have an effect on the character, development, or behavior of

someone or something (Oxford English Dictionary, 1989). Cheung, Lee and Rabjohn (2008)

investigated the factors that drive customers to adopt messages online. They built off of the

model of information adoption developed by Sussman & Siegal (2003). Social media posts

can be deemed influential as a result of perceived information quality, perceived source

credibility, and perceived information usefulness (Cheung et al., 2008). Information quality

can be divided into information relevance, timeliness, accuracy, and comprehensiveness.

Source credibility can be divided into source expertise and trustworthiness and information

usefulness can be defined as valuable, informative, and helpful (Cheung et al., 2008). The

model used in the study assumed information quality and source credibility impact

information usefulness which in turn influences information adoption. The particular method

that was employed by Cheung et al. (2008) simulated an online platform with comments and


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then measured each of the influence constructs using a 7-point Likert scale. The study found

that the components of information quality did impact information usefulness and therefore

adoption but it was hard to measure source credibility in this environment (Cheung et al.,

2008). Nonetheless, this model has been used in other research regarding online influence.

Rieh and Belkin (1998) found that people particularly depend more on source authority and

credibility in the web environment than in the print environment. This is because they are

somewhat forced to assess information quality on the basis of limited information indicating

credibility. Rieh and Belkin (2000) conducted a study that proved respondents are concerned

with information quality and cognitive authority in the online environment and tend to make

predictive judgements based on available information. Source credibility is a significant factor

in determining the effectiveness of persuasive communication (Coulter & Roggeveen, 2012).

Coulter and Roggeveen (2012) found that credibility perceptions on Facebook and Twitter

leads to increased knowledge and liking. In order for communication to be influential it must

both contain quality information and be from a credible source. This presents difficulties for

online users as they must make credibility judgements based off of limited information about

the source.

Perceived Credibility Online

The literature maintains that influence can be categorized by source credibility, information

quality, and information usefulness (Cheung et al., 2008; Metzger, Flanigan, & Medders,

2010). Coulter and Roggeveen (2012) sought to analyze persuasive word-of-mouth

communications on both Facebook and Twitter. The study solidified that source credibility is

a significant factor in determining the effectiveness of persuasive communications online.

Rieh and Belkin (1998) conducted a study in the form of in-depth interviews to investigate

criteria for evaluating websites. They found that people are often forced to assess the quality

of a website on the basis of source authority and credibility rather than information content

and other criteria that can often be used in a traditional environment. The interviews proved

that people depend on source authority and credibility more in the web environment than they

do in the traditional print environment. However, Rieh and Belkin (1998) also found that


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source credibility is not as easily identified in the web environment and therefore people are

often forced to make use of new characteristics of web information in order to make

judgements about authority and credibility. This study solidified that challenges arise when

credibility judgements need to be determined in an online environment. It points to the

necessity of identifying these “new characteristics” of web information that people use to

assess credibility. In 2000, Rieh and Belkin built off of their earlier study addressing

information quality and cognitive authority in the web. While their previous study gathered

data through retrospective accounts, this study assessed actual searching behavior. The results

contended that subjects made both predictive and evaluative judgements based on their

perceptions of cognitive authority. Responses were coded as information quality when

subjects mentioned the website “would be” good, best, accurate, correct, or current.

Responses were coded as cognitive authority when subjects mentioned the information or

source selected was trustworthy, credible, reliable, reputable, or respectable. The study also

coded criteria that led the subjects to make judgements of information quality and cognitive

authority. Some of these criteria included organization/structure, graphics, presentation,

reputation of source, and URL organization type. The implications of this study are two-fold.

The findings solidify the idea that users assess source credibility and information quality to

determine if they should even visit a website and then again to decide if they should stay on

the website. The findings also indicate that assessments of credibility and quality online often

rely on extra-message factors (Rieh & Belkin, 2000).

Several other studies have solidified the notion that challenges arise when source credibility

needs to be determined in an online environment. Past studies also suggest that users rely on

other visible clues in order to make judgements about the credibility of information. Morris,

Counts, Roseway, Hoff and Schwartz (2012) conducted a study to evaluate credibility

perceptions on Twitter. The study found that respondents tend to focus on features that are

immediately visible in the interface when making credibility judgements. This means that the

ability for users to judge credibility is limited to features that are visible at a glance (Morris et

al., 2012). These features included follower count, number of retweets, and mention count.

Westerman, Spence and Van Der Heide (2012) conducted a study that exposed users to mock


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Twitter pages and then evaluated perceptions of source credibility. The results suggested that

when the content of a message is held constant across conditions, people rely primarily on

heuristic judgements of system-generated cues. The heuristic cues in this study were numbers

of followers as well as ratio between followers and following. This study was particularly

significant as message content was held constant so differences in credibility judgements were

based solely on manipulation of follower count. Westerman, Spence & Van Der Heide built

off of this research in 2014 and investigated how pieces of information available on social

media impact perceptions of source credibility. The respondents were exposed to three

different mock Twitter pages and answered subsequent questions regarding source credibility

and cognitive elaboration. The data suggested that there are certain heuristic cues that people

rely on when making judgements about information on social media in addition to follower

count. Metzger et al. (2010) built off the idea that users typically use cognitive heuristics to

evaluate credibility online and set up a series of focus groups. The focus groups suggested

that users tend to rely on heuristics such as number of testimonials and layout when

evaluating the credibility of product information on website (Metzger et al., 2010). Wathen

and Burkell (2002) conducted a literature review of past studies investigating source

credibility online. After analysis of the literature, Wathen and Burkell (2002) confirmed that

assessing credibility online is inherently different than assessing credibility in a traditional

sense. In particular, surface aspects of a website are imperative to credibility judgements

online. Users actually judge many aspects of information through their perceptions of

credibility. Ultimately, Wathen and Burkell (2002) confirmed that the source characteristics

that affect credibility judgements online are inherently different than characteristics that affect

traditional communications and suggested that future research be conducted to define the key

markers of credibility online.

Influence and Social Media Metrics

Literature regarding source credibility online confirms that credibility is assessed differently

online than it is in a traditional setting. The literature maintains that users often invoke

cognitive clues when it comes to evaluating perceived credibility online. In order for

marketers to understand influence on social media it is imperative to understand which


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particular heuristic clues are used to evaluate social media messages. Big data in the form of

social media metrics are visible and accessible indicators of popularity and help convert

credibility and likeability to economic capital (Baym, 2013). The “Like Economy”

particularly places heavy emphasis on the value of social activities (Gerlitz & Helmond,

2011). An empirical study conducted by Gerlitz & Helmond (2011) suggests that low

numbers in the “counter” section of a website (indicating number of likes, shares, etc…)

implies that website visitors were not engaged. The “counter” section of a website shows how

many times the page has been recommended through the use of social buttons. These social

buttons are indicative of the importance assigned to both “liking” and “sharing” and

contribute to the content validation of websites (Gerlitz & Helmond, 2011). Facebook also has

several social buttons that are visible on both posts and product pages. Phua and Ahn (2014)

aimed to investigate the readily-available information on Facebook pages that lead to

consumers’ perceptions of the brand. The study manipulated both number of overall likes and

number of friends’ likes on Facebook brand pages. An online questionnaire was used to assess

perceptions of credibility, trustworthiness, and purchase intention. The results suggest that the

higher number of likes (overall and friend’s likes) leads to positive brand attitude, brand trust,

and purchase intention. This study indicates that users are influenced by the number of likes

on a Facebook page.

Van der Heijden (2013) investigated how number of likes, comments, and shares on Facebook

messages can affect user’s perceptions. The study measured both attitude and interaction

intentions. The findings suggest that number of likes, comments, and shares did not affect

attitudes or intention to “like”, “comment”, or “share” the post. Van der Heijden (2013)

believed that a potential reason for this was that the users were highly involved in the

information seeking process (viewing news information) and therefore took the central route

to persuasion and ignored the peripheral cues. The finding that number of likes, comments,

and shares on a post did not impact the likelihood of another user liking, commenting, or

sharing the post were consistent with the findings of Winter (2015). Winter (2015) found that

number of likes and followers on Twitter and Facebook typically indicate popularity but do

not alone influence users to like or follow the brand. Winter (2015) used survey research and


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Likert scale questions to investigate influence based on number of likes and followers. In fact,

65% of users disagreed that they are more likely to “like” a Facebook page if it has a large

number of likes. Conversely, Egebark and Ekström (2011) found that users do tend to “like”

something online solely because someone before them has. Egebark and Ekström (2011)

tracked 44 different status updates throughout a 7 month period. The study divided the

Facebook friends into two groups: control and treatment. The two groups received identical

status updates but for the “treated” individuals a “like” from an unknown user was added to

the post. Ultimately, the study revealed that if three or more people “liked” the status update,

the user was inclined to “like” it as well.

Antonopoulos, Giomelakis, Veglis and Dimoulas (2016) also investigated how social media

metrics play a role in shaping user’s opinions about a website. This study utilized an online

questionnaire and respondents indicated that a larger number of likes and shares will have a

greater impact on the opinions of others than it would on their own opinions. These results

suggest that users often believe that message source characteristics will have a greater effect

on others than themselves. Nonetheless, the research contends that social media metrics

should be taken into serious consideration as they have an effect on user’s opinions of

credibility and trustworthiness of a website. Similarly, Stavrositu and Kim (2014) conducted a

study that investigated the role played by social media metrics displayed alongside a news

story in shaping user’s perceptions of content and influence. The news story was identical

across all conditions in the study and only the number of social media metrics were

manipulated. The findings indicated that users claim that a news story with a low amount of

social media metrics will have a greater impact on others than themselves. Conversely, users

indicated that a news story with high social media metrics will create similar perceptions of

influence for themselves and others because higher metrics serve to enhance perceived

normative influence. This study suggests that low metrics can indicate a message is

undesirable while high metrics increases desirability and perceived impact for both others and

themselves (Stavrositu & Kim, 2014).


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Conclusion

It is undisputed in the literature that social media is imperative to the marketing concept. With

the rise of the internet and various social media platforms, users often look online when it

comes to making purchase decisions. Social media marketing can help companies to leverage

their brand and increase visibility at virtually no cost (Brettel et al., 2015; Wong, 2017;

DeMers 2017; Mangold & Faulds, 2009). Social media also facilitates electronic word-of-

mouth communications. Electronic word-of-mouth serves as another way to promote a brand

at an unrivaled speed. Electronic word-of-mouth is often seen as more influential than

traditional print advertisements as it is seen as faster and more convenient (Sun et al., 2006;

Phelps et al., 2004). The literature contends that online influence can be broken down into

perceived source credibility, information quality, and information usefulness (Cheung et al.,

2008; Rieh & Belkin, 1998; Coulter & Roggeveen, 2012). Source credibility in particular is

essential to social media influence as it is often difficult to judge content quality online

(Coulter & Roggeveen, 2012; Morris et al., 2012; Westermann et al., 2012). The wide array of

information available online also makes perceived credibility imperative to influence.

However, assessments of source credibility online also presents inherent challenges to users.

Users often have to make judgements about the credibility of a source of information on the

basis of various clues (Rieh & Belkin, 2000; Morris et al., 2012; Westermann et al., 2012;

Wathen & Burkell, 2002). Some studies have revealed that quantity of social media metrics

are an essential indicator of quality when it comes to websites and some social media

platforms (Metzger et al., 2010; Gerlitz & Helmond, 2011; Phu & Ahn, 2014; Van der

Heijden, 2013; Antonopoulos et al., 2016; Stavrositu & Kim, 2014). However, further

information is needed to determine if social media metrics are the key markers of online

influence. Research involving the elaboration likelihood model contends that individuals

process information differently depending on their level of involvement in a particular

situation (Petty & Cacioppo, 1986). This leaves a gap to further explore how a user’s

perception of influence changes depending on their level of involvement. The proposed study

will attempt to measure how quantity of social media metrics and level of involvement impact

a user’s perception of a company’s social media message. The hypotheses that will be tested

in the study are as follows:


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H1: There is a significant relationship between the number of overall likes, comments

and shares on a Facebook post and purchase intention

H1a: The size of this effect will be greater in the low-involvement situation


and shares on a Facebook post and perceived source credibility



and shares on a Facebook post and perceived information quality



and shares on a Facebook post and perceived information usefulness


H5: Facebook posts accompanied by a higher amount of likes, comments, and shares

are more likely to be liked, commented or shared


METHODS Design

The method employed in this study was a 2x2 factorial design (Involvement/Number of likes,

comments, shares). Each participant was exposed to one of the conditions at random which all

involved the purchase of a vacation. Each condition was a Facebook post from a travel agency

encouraging the participant to consider using them to purchase their trip. The message content

was held constant across conditions. The picture used in the post was also held constant

across conditions. The picture was generic and vague to be sure to not bias participants

toward a particular vacation destination. Participants were told to assume they had the

financial capability and time to go on the trip.


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Data Collection

A total of 250 participants were surveyed for this study. Participants were primarily college

sophomores, juniors, or seniors (ages 18-25) as the behavioral lab at Bryant University was

used to survey respondents and some students received class credit for their participation. The

survey was created on Qualtrics and each respondent completed it on the computer. The

respondents received one of the conditions at random. Ultimately, 51% of the participants

who took the survey were male and 49% were female. 90% of participants had traveled at

least one time in the past year and 93% of participants use Facebook with over half using it

daily. Ethical approval was given by the Bryant University Institutional Review Board prior

to data collection.

Measures

The first independent variable measured was the user’s involvement in the situation.

Involvement was manifested in the amount of time the user had to make the decision. The

user was either told that they had 24 hours to make the decision in order to secure a deal

(short deadline/high involvement) or they had 6 months to make the decision and were simply

browsing (long deadline/low involvement).

The second independent variable was quantity of social media metrics (likes, comments, and

shares). The Facebook post the user viewed either had 1,000 likes, 200 comments, and 100

shares (high metrics) or 5 likes, 1 comment, and 0 shares (low metrics). The user was required

to view the Facebook post for a full 40 seconds before they were allowed to proceed with the

survey. The high and low amount of metrics were decided after searching other travel

agencies on Facebook and evaluating the number of likes, comments, and shares that they

typically generate on each of their posts.

The first set of follow-up questions served as a manipulation check. They asked the user to

indicate if the post they just viewed had a large number of likes, comments, and shares. They

also were asked to indicate if the scenario described required them to make a decision in a

short amount of time. The manipulation checks were measured using 7-point Likert scale


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questions. The questions served to ensure the user was aware of the situation and the post they

viewed.

The second set of questions measured the participant’s interaction intention. These questions

asked how likely the participant was to like, comment, and share the post they just viewed.

They also responded to a question that measured their likelihood of clicking the link

mentioned in the post and likelihood of booking the trip (purchase intention). Each of these

constructs were measured using 7-point Likert scale questions.

The overall goal of this study was to measure how influential the Facebook posts were.

Influence in this study is defined as perceived source credibility, perceived information

quality, and perceived information usefulness as modeled in the study by Cheung et al.

(2008). The next set of questions measured perceived source credibility. These questions used

the expertise and trustworthiness components of the credibility scale developed by Ohanian

(1990). For source trustworthiness, participants were asked to indicate if the post was

perceived to be dependable, honest, reliable, sincere and trustworthy. For source expertise,

participants were asked to indicate if the post was perceived to be expert, experienced,

knowledgeable, qualified, and skilled. Both trustworthiness and expertise were measured with

a 7-point semantic differential scale. Reliability analysis yielded a Cronbach alpha= .955 so

the 10 items were averaged to form an index for source credibility.

The next set of questions measured perceived information quality. These questions used the

relevance and accuracy components of the information quality assessment modeled by

Cheung et al. (2008) from a study by Wixom and Todd (2005). For information relevance,

participants were asked to indicate if the post was perceived to be relevant, appropriate, and

applicable. For information accuracy, participants were asked to indicate if the post was

perceived to be accurate, correct, and reliable. Both relevance and accuracy were measured

with a 7-point Likert scale. Reliability analysis yielded a Cronbach alpha= .906 so the 6 items

were averaged to form an index for information quality.


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The next set of questions measured perceived information usefulness. These questions used

the information usefulness assessment modeled by Cheung et al. (2008) from a study by

Bailey and Pearson (1983). Participants were asked if they perceived the post to be valuable,

informative, and helpful. Information usefulness was measured with a 7-point Likert

scale. Reliability analysis yielded a Chronbach alpha= .920 so the 3 items were averaged to

form an index for information usefulness.

The last set of questions measured demographic information about each participant. There

was also a set of questions that measured open-ended, exploratory information regarding what

makes the user typically like, comment, or share something on Facebook.

ANALYSIS & RESULTS Manipulation Checks

Manipulation checks were performed to assess the effectiveness of the variables. Involvement

(Short deadline or long deadline) was tested using a one-way Analysis of Variance (ANOVA)

with deadline as the independent variable and the manipulation as the dependent variable. The

test revealed that respondents in the “short deadline” category indicated that the scenario

“required them to make a decision in a short amount of time” significantly more than

respondents in the “long deadline” category (MShort= 4.769, MLong= 3.669, F(1,248)= 29.087, p<.001). The metrics manipulation (High likes/comments/shares or low likes/comments/shares) was

tested with three one-way ANOVAs with metrics as the independent variable and the

manipulation as the dependent variable. The test revealed that respondents in the “high

metrics” category indicated that the post had “a lot of likes” significantly more than

respondents in the “low metrics” category (MHigh= 11.81, MLong= .9.323, F(1,248)= 222.657, p<.001). The

second test revealed that respondents in the “high metrics” category indicated that the post

had “a lot of comments” significantly more than respondents in the “low metrics” category

MHigh= 4.341,, MLow= 1.831, F(1,248)= 209.748, p<.001). The third test revealed that respondents in the

“high metrics” category indicated that the post “had a lot of shares” significantly more than


- 18 -

respondents in the “low metrics” category (MHigh= 4.159, MLow= 2.169, F(1,248)= 143.898, p<.001). (See

Appendix H).

Hypotheses Tests

Hypothesis 1: Hypothesis 1 predicted that there is a significant relationship between number

of likes, comments, and shares on a Facebook post and purchase intention. Hypothesis 1 was

tested using a two-way ANOVA with deadline and metrics as the independent variables and

purchase intention as the dependent variable. As predicted, the main effect for metrics was

significant F(1, 246)= 3.876, p=.05). As shown in Figure 1, participants in the “high metrics”

condition had significantly greater purchase intention than participants in the “low metrics”

condition (MHigh= 3.405, MLow= 2.988, F(1,246)= 3.876, p=.05). Thus, H1 is supported.

Figure 1 – Metrics and Purchase Intention


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Hypothesis 1a: Hypotheses 1a predicted that the size of the effect of Hypothesis 1 will be

greater in the low-involvement situation (low involvement=long deadline). As predicted, the

Metrics x Deadlines two-way interaction effect emerged (F(1, 246) = 2.943, p = .087). As

shown in Figure 2, contrasts revealed that participants in the High Metrics condition (Mhigh =

3.375) were more likely to book the trip than participants in the Low Metrics condition (Mlow

=2.594) when participants had a six month deadline (F(1, 131) = 8.14, p < .01), but participants

in the Low Metrics condition (Mlow = 3.38) and participants in the High Metrics condition (Mhigh=

3.45) showed no significant difference in booking the trip (F(1, 115) = .027, p = .n.s.) when

participants had a 24-hour deadline. Thus, H1a is supported.

Figure 2 – Metrics x Involvement and Purchase Intention

Hypothesis 2: Hypothesis 2 predicted that there is a significant relationship between the

number of overall likes, comments and shares on a Facebook post and perceived source

credibility. Hypothesis 2 was tested using a two-way ANOVA with deadline and metrics as

the independent variables and source credibility as the dependent variable. As predicted, the

main effect for metrics was significant (F(1, 246)= 8.450, p=<.01). As shown in Figure 3,

participants in the “high metrics” condition perceived source credibility to be significantly


- 20 -

greater than participants in the “low metrics” condition (MHigh= 4.287, MLow= 3.838, F(1,246)= 8.450,

p<.01). Thus, H2 is supported.

Figure 3 – Metrics and Source Credibility

Hypothesis 2a: Hypothesis 2a predicted that the size of the effect of Hypothesis 2 will be

greater in the low-involvement situation (low involvement=long deadline). A two-way

interaction between deadline and metrics was not evident (F(1,246)= .114, p= n.s.). Therefore,

H2a was not supported.

Hypothesis 3 & 3a: Hypothesis 3 predicted that there is a significant relationship between the

number of overall likes, comments and shares on a Facebook post and perceived information

quality. Hypothesis 3a predicted that this effect would be greater in the low-involvement

situation. Hypothesis 3 was tested using a two-way ANOVA with deadline and metrics as the

independent variables and information quality as the dependent variable. There was neither a

main effect for metrics nor an interaction effect for metrics and deadline. Therefore, H3 and

H3a were not supported.


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Hypothesis 4: Hypothesis 4 predicted that there is a significant relationship between the

number of overall likes, comments and shares on a Facebook post and perceived information

usefulness. Hypothesis 4 was tested using a two-way ANOVA with deadline and metrics as

the independent variables and perceived information usefulness as the dependent variable. As

predicted, the main effect for metrics was significant (F(1, 246)= 4.076, p<.05). As shown in

Figure 4, participants in the “high metrics” condition perceived information usefulness as

being significantly greater than participants in the “low metrics” condition (MHigh= 4.453 MLow= 4.078,

F(1,246)= 4.076, p<.05). Thus, H4 is supported.

Figure 4 – Metrics and Information Usefulness

Hypothesis H4a: Hypothesis 4a predicted that the size of the effect of Hypothesis 4 will be





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Hypothesis 5: Hypothesis 5 predicted that Facebook posts accompanied by a higher amount of

likes, comments, and shares are more likely to be liked, commented or shared. As predicted,

the main effect for metrics was significant, but only when it came to commenting (F(1, 246)=

4.645, p<.05). As shown in Figure 5, participants in the “high metrics” condition were

significantly more likely to “comment” on the post than participants in the “low metrics”

condition (MHight= 2.263, MLow= 1.881, F(1,246)= 4.645, p<.05). However, the likelihood of “liking” and

“sharing” the post were not significant. Thus, H5 is partially supported.

Figure 5 – Metrics and Commenting

Hypothesis H5a: Hypothesis 5a predicted that the size of the effect of Hypothesis 5 will be




DISCUSSION & IMPLICATIONS Past research contends that the influence of marketing communications are perceived

differently online than they are in a traditional environment (Rieh & Belkin, 1998; Rieh &

Belkin, 2000; Wathen & Burkell, 2002; Morris et al., 2012). These judgements of influence

online are based on perceived source credibility, information quality, and information

usefulness (Cheung et al., 2008; Rieh & Belkin, 1998; Coulter & Roggeveen, 2012). Past

studies in particular have investigated the nature of perceived source credibility online as


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these perceptions are arguably more important online than in a traditional environment

(Coulter & Roggeveen, 2012; Morris et al., 2012; Westermann et al., 2012). These studies

pointed to the use of heuristic clues online in order for users to make credibility assessments

based on limited visible information (Rieh & Belkin, 2000; Morris et al., 2012; Westermann

et al., 2012; Wathen & Burkell, 2002). Further research even investigated some of these

heuristic clues such as the numbers in the “counter section” of a website, number of followers

on Twitter, and number of Facebook page likes (Metzger et al., 2010; Gerlitz & Helmond,

2011; Phu & Ahn, 2014; Van der Heijden, 2013; Antonopoulos et al., 2016; Stavrositu &

Kim, 2014). However, none of these studies specified exactly which qualities of social media

are used to make judgements of influence. This study shows the importance of social media

metrics in the form of likes, comments, and shares on Facebook posts.

As predicted, there was a main effect between number of likes, comments, and shares on the

Facebook post and perceived source credibility. Posts with a higher amount of likes,

comments, and shares were viewed as more credible by users. This finding is imperative to

marketers as it proves the importance of focusing efforts on social media marketing.

Facebook posts that generate a high amount of likes, comments, and shares are automatically

assumed to have come from a more credible source. Essentially, a user can evaluate the

credibility of a marketing communication solely based on the number of metrics the post

generated. Therefore, it is essential for companies to create social media posts that will

generate these large numbers of likes, comments, and shares. Social media marketers must

focus their efforts on enhancing their social media posts in order to successfully engage users.

Similarly, there was a main effect between number of likes, comments, and shares on a

Facebook post and perceived information usefulness. Posts with a higher amount of likes,

comments, and shares were viewed as containing more useful information even though the

content of the post was held constant across conditions. This finding is essential for marketers

in the same way that the credibility finding is. By curating content that generates high

amounts of likes, comments, and shares, companies are giving themselves a leg-up. A post


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accompanied by a high number of Facebook metrics is perceived as being more useful by a

user.

As predicted, there was also a main effect between number of likes, comments, and shares on

a Facebook post and purchase intention. Users who viewed a post with more likes, comments,

and shares were more likely to “book the trip” than users who viewed a post with a low

number of likes, comments, and shares. There was also an interaction between metrics and

involvement for purchase intention. In the low involvement situation (users had 6 months to

make a decision on their vacation), users were more likely to book the trip if the post had a

high amount of likes, comments, and shares. In the high-involvement situation there was no

significant difference in purchase intention. This finding is consistent with Petty & Cacioppo

(1984) which found that peripheral cues are more effective in low-involvement situations than

high-involvement situations. This finding is important for marketers as it stresses the

necessity of generating large amounts of likes, comments, and shares on Facebook

particularly when a user is less-involved in the purchase decision. On the other hand, it

demonstrates that high involvement, in the form of a short deadline, leads to increased

purchase intention despite the amount of likes, comments, and shares. Therefore, promotions

that require users to make a decision under time pressure will lead to increased purchase

intention despite the number of metrics generated by the post. If a company does not curate

engaging content that generates large amounts of likes, comments, and shares they can still

enhance sales by giving their customers a deadline.

The results also demonstrate that a higher amount of likes, comments, and shares on a

Facebook post led to increased likelihood of another user commenting on the post. However,

the same was not true for liking and sharing. A high amount of likes, comments, and shares

did not lead to increased likelihood of another user liking or sharing the Facebook post. Since

consumers today are heavily dependent on electronic word-of-mouth, (Sun et al., 2006;

Phelps et al., 2004) it is important for companies to generate comments on their Facebook

posts since these comments essentially serve as word-of-mouth marketing. Therefore, social


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media marketers must create content that generates a large amount of likes, comments, and

shares so that users are more likely to comment on the post.

The findings of this study are essential to marketers as they demonstrate the importance of

creating social media posts that generate high amounts of metrics. However, it is also

imperative for marketers to understand how to create posts that will generate large quantities

of likes, comments, and shares. This study also contained an exploratory component that

asked respondents to indicate what makes them like, comment, or share a post on Facebook.

These findings can give marketers a better understanding of what encourages users to engage

with a post. When it came to “liking” a post, 22% of respondents indicated that they will

“like” a post on Facebook if it is relevant/relatable and they agree with it. 20% indicated they

will “like” a post on Facebook if it is funny or entertaining, 18% “like” a post if it is

interesting and 25% will “like” a post simply because they enjoy it. When it came to

commenting, 15% of respondents indicated they will comment on a post if it is relevant,

legitimate, and relates to them in some way. 12% indicated they will comment if they have a

strong opinion or feeling about the post and 13% indicated that they will comment if the post

is funny or entertaining. When it came to “sharing” a post, 24% of respondents indicated they

will share a post because they want other people to see it. 22% of respondents indicated they

will share a post if it is interesting, relevant, or relatable and 10% indicated they will share a

post simply because they enjoy it. This information helps marketers understand what drives

users to engage with Facebook posts. The broader implication would be for companies to

engage in social listening and understanding their consumers so they can leverage these

findings. Once marketers understand what type of content resonates with their consumers,

they will be able to enhance their social media strategy.

Marketers must aim to understand what type of information their customers find entertaining,

informative, relevant, and interesting. A study performed by De Vries, Gensler and Leeflang

(2012) aimed to discover the qualities that lead to enhanced numbers of likes and comments

on social media posts. The results suggested that number of likes can be increased by posting

vivid and interactive posts such as videos or contests. The results also suggested that a large


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amount of comments increases the amount of likes because it enhances the attractiveness of

the post (De Vries, Gensler & Leeflang, 2012). Number of comments can be enhanced by

creating posts that are interactive in the form of a question. However, low-level interactive

posts such as URLs have a negative impact on number of comments (De Vries, Gensler &

Leeflang, 2012). This is because clicking on a link navigates the user away from the Facebook

page (Sabate, Berbegal, Canabate & Lebherz, 2014). Sabate et al. (2014) also found that posts

containing images significantly increases both number of comments and number of likes.

Posts that are published during business hours are also more likely to be commented on as

people are typically viewing Facebook from a desktop instead of their mobile device (Sabate

et al., 2014). Tafesse (2015) tracked 191 brand posts from Facebook brand pages in the UK in

order to understand what drives number of likes and shares. The results demonstrated that

vividness, brand consistency, and novel content led to increased audience response. People

often connect with companies on social media in order to join a brand community. Therefore,

posts that are consistent with the brand resonate with these users (Tafesse, 2015). The data

suggested that companies should create fresh and creative content and that entertaining

content receives more engagement overall than informational content. Companies can use the

Facebook insights feature to understand what type of content is the most successful and

generates the most engagement (DeMers, 2015).

Kumar and Mirchandani (2012) identified a framework for successful social media marketing.

This framework centered on the idea of identifying influential users that can spread the

company’s message to their audience. These influential users generate high levels of

engagement through message spread and social impact (number of metrics). The framework

suggests that if companies can locate these influential people they can serve as brand

ambassadors and spread positive word-of-mouth. Companies can offer these influential users

incentives to promote their products and services. Brand ambassadors help provide visibility

to the brand and boost overall awareness (Torr, 2015). They ultimately serve as a way to

strengthen the relationship between the customer and the product by humanizing the brand.

Brand ambassadors can help a company to increase their social reach because they have

strong online reputations and an established follower base (Torr, 2015). Brand ambassadors


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can facilitate word-of-mouth and help to protect the company’s reputation (Torr, 2015). These

influential users are typically not paid and therefore often appear as more credible to the

consumer (Urbaniak, 2017). Companies can find brand ambassadors because they are active

on the web and generate high amounts of engagement. This is a cost-effective marketing

strategy as it enables companies to spread their message at virtually no cost (Urbaniak, 2017).

These influential brand ambassadors already generate high amounts of social media metrics

so by entering into a partnership with them, the company will enhance its influence as well.

LIMITATIONS AND FUTURE RESEARCH The sample used in this survey was limited to Bryant University students between the ages of

18-25 as the behavioral lab at the University was used to collect results. This sample places

limitations on the ability to generalize the findings of the study. Future research could

investigate how other demographics perceive the influence of Facebook marketing. This

would enable researchers to see if the findings remain the same across other age groups and

geographical locations. Similarly, further research could also collect results outside of a lab

setting.

The product used in this study was the purchase of a vacation. The idea of a vacation could

have potentially biased results since the product is intangible and college-aged students may

not have experience booking a trip. Future research could choose a tangible product from a

different industry that college students are more familiar with purchasing. While this study

manipulated involvement by creating a situation with a short or long deadline, future research

could also choose to manipulate involvement in a different way. Future studies could also aim

to manipulate metrics to see what number of likes, comments, and shares make a post

influential and if there is a certain cut-off point.

The findings of the study are also inherently limited as the study measured purchase intention

and not actual purchase habits. Future research could find a way to see if consumers who view

certain social media posts end up actually purchasing the product. This would determine if

people who indicate that they would do something will actually do it when the time comes.


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Similarly, this study asked users how likely they were to like, comment, and share the post.

Future research could measure click-thru-rates to see if users actually like, comment, or share

a post as opposed to simply asking them if they would.

This study attempts to bridge the gap in literature in terms of social media metrics and

perceptions of influence. The study focuses on the importance of visible social media metrics

on Facebook when it comes to determining influence. Further research could focus on visible

social media metrics on other platforms such as Twitter and Instagram. Future research in this

area would stress the importance of companies creating an integrated social media marketing

strategy.

CONCLUSION Social media users today are often fixated on generating a large amount of visible social

media metrics. Everyone from individual users to companies to celebrities are vying for a

large amount of “likes” on their social media posts. This study proved that the importance of

generating high amounts of social media metrics goes beyond popularity or self-esteem.

Number of Facebook likes, comments, and shares are imperative to consumer’s perceptions of

social media marketing messages. Users rely on heuristic clues to make assessments of

perceived credibility, information quality, and information usefulness online. On Facebook in

particular, these assessments appear to be made on the basis of likes, comments, and shares.

When a post generates likes, comments, and shares it is automatically given more visibility as

a result of Facebook algorithms. However, this study asserts that the importance of these

metrics goes beyond Facebook algorithms. This study emphasizes the importance of

generating high amounts of likes, comments, and shares on Facebook as it leads to increased

purchase intention, perceived source credibility, and perceived information usefulness. Past

research has indicated that users rely on certain visible clues when they evaluate influence

online. This study suggests that likes, comments, and shares on Facebook serve as these

markers of influence. This study offers practical implications for social media marketers and

adds to research on social media marketing, electronic word-of-mouth, the elaboration

likelihood model, online influence, and social media metrics.


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APPENDICES Appendix A – High and Low Metrics Condition


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- 31 -

Appendix B – Manipulation Check Survey Items


- 32 -

Appendix C – Interaction Intention Survey Items


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Appendix D – Perceived Source Credibility Survey Items


- 34 -

Appendix E – Perceived Information Quality & Usefulness Survey Items


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Appendix F – Demographic & Exploratory Survey Items


- 36 -


- 37 -

Appendix G– Key Demographics Breakdown

Male49%

Female51%

Male

Female

10%

23%

31%

36%

How Often Did You Travel in the Last Year?

Not at all

Once

Twice

3+


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Appendix H – Manipulation Check Analysis

9

9.5

10

10.5

11

11.5

12

Metrics_Lo Metrics_Hi

The post you just viewed had a lot of "likes"

1.5

2

2.5

3

3.5

4

4.5


The post you just viewed had a lot of comments


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2

2.5

3

3.5

4

4.5


The post you just viewed had a lot of shares

3

3.2

3.4

3.6

3.8

4

4.2

4.4

4.6

4.8

5

Dead_Long Dead_Short

"The situation described required you to make a decision in a short amount of time"


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Appendix I– Exploratory Findings Breakdown


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